Results 1  10
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22
Three Main Concerns in Sketch Recognition and an Approach to Addressing Them
, 2002
"... curvilinear configurations to handdrawn sketches. It collects observations from our own recent research, which focused initially on the domain of sketched human stick figures in diverse postures, as well as related computer vision literature. Sketch recognition, i.e., labeling strokes in the i ..."
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Cited by 38 (0 self)
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curvilinear configurations to handdrawn sketches. It collects observations from our own recent research, which focused initially on the domain of sketched human stick figures in diverse postures, as well as related computer vision literature. Sketch recognition, i.e., labeling strokes in the input with the names of the model parts they depict, would be a key component of higherlevel sketch understanding processes that reason about the recognized configurations. A sketch recognition technology must meet three main requirements. It must cope reliably with the pervasive variability of hand sketches, provide interactive performance, and be easily extensible to new configurations. We argue that useful sketch recognition may be within the grasp of current research, if these requirements are addressed systematically and in concert.
PAClearnability of Probabilistic Deterministic Finite State Automata
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2004
"... We study the learnability of Probabilistic Deterministic Finite State Automata under a modified PAClearning criterion. We argue that it is necessary to add additional parameters to the sample complexity polynomial, namely a bound on the expected length of strings generated from any state, and a ..."
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Cited by 35 (8 self)
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We study the learnability of Probabilistic Deterministic Finite State Automata under a modified PAClearning criterion. We argue that it is necessary to add additional parameters to the sample complexity polynomial, namely a bound on the expected length of strings generated from any state, and a bound on the distinguishability between states. With this, we demonstrate that the class of PDFAs is PAClearnable using a variant of a standard statemerging algorithm and the KullbackLeibler divergence as error function.
Learning Regular Languages From Simple Positive Examples
, 2000
"... Learning from positive data constitutes an important topic in Grammatical Inference since it is believed that the acquisition of grammar by children only needs syntactically correct (i.e. positive) instances. However, classical learning models provide no way to avoid the problem of overgeneralizati ..."
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Cited by 29 (0 self)
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Learning from positive data constitutes an important topic in Grammatical Inference since it is believed that the acquisition of grammar by children only needs syntactically correct (i.e. positive) instances. However, classical learning models provide no way to avoid the problem of overgeneralization. In order to overcome this problem, we use here a learning model from simple examples, where the notion of simplicity is defined with the help of Kolmogorov complexity. We show that a general and natural heuristic which allows learning from simple positive examples can be developed in this model. Our main result is that the class of regular languages is probably exactly learnable from simple positive examples.
Probabilistic FiniteState Machines  Part I
"... Probabilistic finitestate machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition and machine translatio ..."
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Cited by 27 (1 self)
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Probabilistic finitestate machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked: computational linguistics, machine learning, time series analysis, circuit testing, computational biology, speech recognition and machine translation are some of them. In part I of this paper we survey these generative objects and study their definitions and properties. In part II, we will study the relation of probabilistic finitestate automata with other well known devices that generate strings as hidden Markov models and ngrams, and provide theorems, algorithms and properties that represent a current state of the art of these objects.
Grammar Inference, Automata Induction, and Language Acquisition
 Handbook of Natural Language Processing
, 2000
"... The natural language learning problem has attracted the attention of researchers for several decades. Computational and formal models of language acquisition have provided some preliminary, yet promising insights of how children learn the language of their community. Further, these formal models als ..."
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Cited by 27 (2 self)
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The natural language learning problem has attracted the attention of researchers for several decades. Computational and formal models of language acquisition have provided some preliminary, yet promising insights of how children learn the language of their community. Further, these formal models also provide an operational framework for the numerous practical applications of language learning. We will survey some of the key results in formal language learning. In particular, we will discuss the prominent computational approaches for learning different classes of formal languages and discuss how these fit in the broad context of natural language learning.
Mining Process Models with NonFreeChoice Constructs
"... Process mining aims at extracting information from event logs to capture the business process as it is being executed. Process mining is particularly useful in situations where events are recorded but there is no system enforcing people to work in a particular way. Consider for example a hospital w ..."
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Cited by 25 (3 self)
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Process mining aims at extracting information from event logs to capture the business process as it is being executed. Process mining is particularly useful in situations where events are recorded but there is no system enforcing people to work in a particular way. Consider for example a hospital where the diagnosis and treatment activities are recorded in the hospital information system, but where healthcare professionals determine the “careflow”. Many process mining approaches have been proposed in recent years. However, in spite of many researchers ’ persistent efforts, there are still several challenging problems to be solved. In this paper, we focus on mining nonfreechoice constructs, i.e., situations where there is a mixture of choice and synchronization. Although most reallife processes exhibit nonfreechoice behavior, existing algorithms are unable to adequately deal with such constructs. Using a Petrinetbased representation, we will show that there are two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. We propose an algorithm that is able to deal with both kinds of dependencies. The algorithm has been implemented in the ProM framework and experimental results shows that the algorithm indeed significantly improves existing process mining techniques.
Probabilistic FiniteState Machines  Part II
"... Probabilistic finitestate machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked. In part I of this paper, we surveyed these objects and studied their properties. In this part II, we study the relations between probabilistic finit ..."
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Cited by 12 (2 self)
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Probabilistic finitestate machines are used today in a variety of areas in pattern recognition, or in fields to which pattern recognition is linked. In part I of this paper, we surveyed these objects and studied their properties. In this part II, we study the relations between probabilistic finitestate automata and other well known devices that generate strings like hidden Markov models and n grams, and provide theorems, algorithms and properties that represent a current state of the art of these objects.
Learning regular expressions from noisy sequences
 In Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation, SARA’05
, 2005
"... Abstract. The presence of long gaps dramatically increases the difficulty of detecting and characterizing complex events hidden in long sequences. In order to cope with this problem, a learning algorithm based on an abstraction mechanism is proposed: it can infer the general model of complex event ..."
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Cited by 4 (0 self)
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Abstract. The presence of long gaps dramatically increases the difficulty of detecting and characterizing complex events hidden in long sequences. In order to cope with this problem, a learning algorithm based on an abstraction mechanism is proposed: it can infer the general model of complex events from a set of learning sequences. Events are described by means of regular expressions, and the abstraction mechanism is based on the substitution property of regular languages. The induction algorithm proceeds bottomup, progressively coarsening the sequence granularity, letting correlations between subsequences, separated by long gaps, naturally emerge. Two abstraction operators are defined. The first one detects, and abstracts into nonterminal symbols, regular expressions not containing iterative constructs. The second one detects and abstracts iterated subsequences. By interleaving the two operators, regular expressions in general form may be inferred. Both operators are based on string alignment algorithms taken from bioinformatics. A restricted form of the algorithm has already been outlined in previous papers, where the emphasis was on applications. Here, the algorithm, in an extended version, is described and analyzed into details. 1
On the relationship between Models for Learning in Helpful Environments
 in Proceedings of ICGI 2000, LNAI 1891
, 2000
"... Abstract. The PAC and other equivalent learning models are widely accepted models for polynomial learnability of concept classes. However, negative results abound in the PAC learning framework (concept classes such as deterministic finite state automata (DFA) are not efficiently learnable in the PAC ..."
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Abstract. The PAC and other equivalent learning models are widely accepted models for polynomial learnability of concept classes. However, negative results abound in the PAC learning framework (concept classes such as deterministic finite state automata (DFA) are not efficiently learnable in the PAC model). The PAC model’s requirement of learnability under all conceivable distributions could be considered too stringent a restriction for practical applications. Several models for learning in more helpful environments have been proposed in the literature including: learning from example based queries [2], online learning allowing a bounded number of mistakes [14], learning with the help of teaching sets [7], learning from characteristic sets [5], and learning from simple examples [12,4]. Several concept classes that are not learnable in the standard PAC model have been shown to be learnable in these models. In this paper we identify the relationships between these different learning models. We also address the issue of unnatural collusion between the teacher and the learner that can potentially trivialize the task of learning in helpful environments. Keywords: Models of learning, Query learning, Mistake bounded learning, PAC learning, teaching sets, characteristic samples, DFA learning.